Erratum: An ultra-sparse code underlies the generation of neural sequences in a songbird

Hahnloser, Richard H. R.; Кожевников, А. А.; Fee, Michale S.

doi:10.1038/nature01221

Cited by 301 publications

(590 citation statements)

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“…However, in a quite similar experimental setup the blockage of LTP leads to no significant differences (Zamanillo 1999). Regarding the neural correlate of memory, potential memory items were found, for example, in songbirds during song and recapitulation as stereotypical sequences of spike burst (Hahnloser et al 2002) or during a spatial task, as spatiotemporal patterns (Harris et al 2003) in the hippocampus. Additionally, typical connectivity structures, so-called motifs, were measured in the cortex (Song et al 2005;Perin et al 2011).…”

Section: Links Between Time Scales Of Memory and Physiologymentioning

confidence: 87%

Time scales of memory, learning, and plasticity

et al. 2012

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If we stored every bit of input, the storage capacity of our nervous system would be reached after only about 10 days. The nervous system relies on at least two mechanisms that counteract this capacity limit: compression and forgetting. But the latter mechanism needs to know how long an entity should be stored: some memories are relevant only for the next few minutes, some are important even after the passage of several years. Psychology and physiology have found and described many different memory mechanisms, and these mechanisms indeed use different time scales. In this prospect we review these mechanisms with respect to their time scale and propose relations between mechanisms in learning and memory and their underlying physiological basis.

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Section: Links Between Time Scales Of Memory and Physiologymentioning

confidence: 87%

Time scales of memory, learning, and plasticity

et al. 2012

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“…Both types of projection neurons generate sparse burst spikes during singing (Hahnloser et al, 2002;Kozhevnikov and Fee, 2007;Prather et al, 2008). In the awake nonsinging state, however, HVC RA neurons are completely inactive (Hahnloser et al, 2002;Kozhevnikov and Fee, 2007;Prather et al, 2008). In contrast, a subpopulation of HVC X neurons exhibits both motor-related activity and auditory re-sponse to the individual bird's own song (BOS) playback and similar songs of other birds .…”

Section: Introductionmentioning

confidence: 97%

“…Therefore, the brain circuits involved in both perception and expression of vocal signals should contain the neurons that transmit syntactic information. In the songbird brain, telencephalic nucleus HVC (used as a proper name) functions in both sensory and motor processing of songs (Nottebohm et al, 1976;Vu et al, 1994;Yu and Margoliash, 1996;Gentner et al, 2000;Hahnloser et al, 2002;Long and Fee, 2008). Hence, analysis of neural activity in Bengalese finch HVC is expected to provide fundamental insights into the neural basis of syntactic organization.…”

Section: Introductionmentioning

confidence: 99%

“…Information processed in HVC is transmitted by two segregated types of projection neurons (Dutar et al, 1998;Mooney, 2000): HVC RA neurons that project to the robust nucleus of the arcopallium (RA), which relay the information to motor neurons for vocal organs (Nottebohm et al, 1976); and HVC X neurons, which innervate to basal ganglia area X (Nottebohm et al, 1982) and are involved in audition-dependent vocal plasticity (Scharff and Nottebohm, 1991;Brainard and Doupe, 2000). Both types of projection neurons generate sparse burst spikes during singing (Hahnloser et al, 2002;Kozhevnikov and Fee, 2007;Prather et al, 2008). In the awake nonsinging state, however, HVC RA neurons are completely inactive (Hahnloser et al, 2002;Kozhevnikov and Fee, 2007;Prather et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Neural Coding of Syntactic Structure in Learned Vocalizations in the Songbird

Fujimoto¹,

Hasegawa

Watanabe

2011

Journal of Neuroscience

109

116

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Although vocal signals including human languages are composed of a finite number of acoustic elements, complex and diverse vocal patterns can be created from combinations of these elements, linked together by syntactic rules. To enable such syntactic vocal behaviors, neural systems must extract the sequence patterns from auditory information and establish syntactic rules to generate motor commands for vocal organs. However, the neural basis of syntactic processing of learned vocal signals remains largely unknown. Here we report that the basal ganglia projecting premotor neurons (HVC X neurons) in Bengalese finches represent syntactic rules that generate variable song sequences. When vocalizing an alternative transition segment between song elements called syllables, sparse burst spikes of HVC X neurons code the identity of a specific syllable type or a specific transition direction among the alternative trajectories. When vocalizing a variable repetition sequence of the same syllable, HVC X neurons not only signal the initiation and termination of the repetition sequence but also indicate the progress and state-of-completeness of the repetition. These different types of syntactic information are frequently integrated within the activity of single HVC X neurons, suggesting that syntactic attributes of the individual neurons are not programmed as a basic cellular subtype in advance but acquired in the course of vocal learning and maturation. Furthermore, some auditory-vocal mirroring type HVC X neurons display transition selectivity in the auditory phase, much as they do in the vocal phase, suggesting that these songbirds may extract syntactic rules from auditory experience and apply them to form their own vocal behaviors.

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“…Second, one can argue that even intuitively "infrequent" events do have a physiological effect. Indeed, the frequency of H3-patterns in relation to a behavior is in the range of the rare single spike activity of many neuronal subpopulations, including numerous cortico-cortical, cortico-thalamic, and cortico-striatal projecting populations in mammals [8,9,10,56,60,66,67,68,71], as well as a group of neurons that connect two song-production nuclei in song birds [30]. Although in most of these cases the functional role of such sparse firing is yet to be determined, it seems to be computationally efficient in visual processing [73] and in songs learning in songbirds [24].…”

Section: Discussionmentioning

confidence: 99%

Precise rhythmicity in activity of neocortical, thalamic and brain stem neurons in behaving cats and rabbits

Dunin-Barkowski

Sirota

Lovering

et al. 2006

Behavioural Brain Research

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Rhythmic discharges of neurons are believed to be involved in information processing in both sensory and motor systems. However their fine structure and functional role need further elucidation. We employed a pattern-based approach to search for episodes of precisely rhythmic activity of single neurons recorded in different brain structures in behaving cats and rabbits. We defined discharge patterns using an algorithmic description, which is different from the previously suggested template methods. We detected episodes of precisely rhythmic discharges, specifically, triads of constant (precision ± 2.5%) inter-spike intervals in the 10-70 ms range. In 54% (67/125) of neurons tested, these patterns could not be explained by random occurrences or by steady or slowly changing input. Rhythmic patterns occurred at a wide range of inter-spike intervals, and were imbedded in nonrhythmic activity. In many neurons, timing of these precisely rhythmic patterns was related to different locomotion tasks or to respiration.

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Erratum: An ultra-sparse code underlies the generation of neural sequences in a songbird

Cited by 301 publications

References 0 publications

Time scales of memory, learning, and plasticity

Time scales of memory, learning, and plasticity

Neural Coding of Syntactic Structure in Learned Vocalizations in the Songbird

Precise rhythmicity in activity of neocortical, thalamic and brain stem neurons in behaving cats and rabbits

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